Overview

Dataset statistics

Number of variables9
Number of observations5707
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory445.9 KiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

basket_size is highly overall correlated with unique_basket_size and 2 other fieldsHigh correlation
unique_basket_size is highly overall correlated with basket_size and 1 other fieldsHigh correlation
gross_revenue is highly overall correlated with basket_size and 2 other fieldsHigh correlation
recency_days is highly overall correlated with qtd_purchasesHigh correlation
qtd_purchases is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
frequency is highly overall correlated with qtd_purchasesHigh correlation
qtd_products is highly overall correlated with basket_size and 2 other fieldsHigh correlation
ticket_size is highly skewed (γ1 = 52.10416182)Skewed
basket_size is highly skewed (γ1 = 48.5851171)Skewed
gross_revenue is highly skewed (γ1 = 21.65063354)Skewed
customer_id has unique valuesUnique

Reproduction

Analysis started2023-02-21 20:19:50.755854
Analysis finished2023-02-21 20:19:58.173572
Duration7.42 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct5707
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16595.205
Minimum12346
Maximum21372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:58.241404image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12697.9
Q114288.5
median16232
Q318215.5
95-th percentile21086.7
Maximum21372
Range9026
Interquartile range (IQR)3927

Descriptive statistics

Standard deviation2755.351
Coefficient of variation (CV)0.16603296
Kurtosis-1.1253584
Mean16595.205
Median Absolute Deviation (MAD)1964
Skewness0.31260909
Sum94708835
Variance7591959.2
MonotonicityStrictly increasing
2023-02-21T17:19:58.335028image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12346 1
 
< 0.1%
17537 1
 
< 0.1%
17567 1
 
< 0.1%
17566 1
 
< 0.1%
17564 1
 
< 0.1%
17562 1
 
< 0.1%
17561 1
 
< 0.1%
17560 1
 
< 0.1%
17557 1
 
< 0.1%
17556 1
 
< 0.1%
Other values (5697) 5697
99.8%
ValueCountFrequency (%)
12346 1
< 0.1%
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
ValueCountFrequency (%)
21372 1
< 0.1%
21371 1
< 0.1%
21370 1
< 0.1%
21369 1
< 0.1%
21368 1
< 0.1%
21367 1
< 0.1%
21366 1
< 0.1%
21365 1
< 0.1%
21364 1
< 0.1%
21363 1
< 0.1%

ticket_size
Real number (ℝ)

Distinct5517
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.256115
Minimum0.42
Maximum84235.525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:58.429268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile3.4763867
Q18.2029754
median16.232254
Q323.85
95-th percentile85.42005
Maximum84235.525
Range84235.105
Interquartile range (IQR)15.647025

Descriptive statistics

Standard deviation1526.7214
Coefficient of variation (CV)24.523236
Kurtosis2761.3261
Mean62.256115
Median Absolute Deviation (MAD)7.9103655
Skewness52.104162
Sum355295.65
Variance2330878.1
MonotonicityNot monotonic
2023-02-21T17:19:58.518855image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.75 11
 
0.2%
4.95 10
 
0.2%
1.25 9
 
0.2%
2.95 9
 
0.2%
7.95 8
 
0.1%
1.65 7
 
0.1%
12.75 7
 
0.1%
8.25 7
 
0.1%
3.35 6
 
0.1%
4.15 6
 
0.1%
Other values (5507) 5627
98.6%
ValueCountFrequency (%)
0.42 3
0.1%
0.535 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.8371428571 1
 
< 0.1%
0.84 2
< 0.1%
0.85 3
0.1%
1.002222222 1
 
< 0.1%
1.02 1
 
< 0.1%
1.03875 1
 
< 0.1%
ValueCountFrequency (%)
84235.525 1
< 0.1%
77183.6 1
< 0.1%
13305.5 1
< 0.1%
4426.521111 1
< 0.1%
3861 1
< 0.1%
3202.92 1
< 0.1%
3096 1
< 0.1%
1900.125 1
< 0.1%
1879.28 1
< 0.1%
1815.2 1
< 0.1%

basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2370
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.17393
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:58.612228image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median152
Q3290.875
95-th percentile733.8125
Maximum74215
Range74214
Interquartile range (IQR)215.875

Descriptive statistics

Standard deviation1197.9558
Coefficient of variation (CV)4.4670853
Kurtosis2774.0511
Mean268.17393
Median Absolute Deviation (MAD)97
Skewness48.585117
Sum1530468.6
Variance1435098.2
MonotonicityNot monotonic
2023-02-21T17:19:58.704713image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 116
 
2.0%
2 72
 
1.3%
3 51
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 29
 
0.5%
12 26
 
0.5%
100 22
 
0.4%
72 22
 
0.4%
88 21
 
0.4%
Other values (2360) 5264
92.2%
ValueCountFrequency (%)
1 116
2.0%
2 72
1.3%
3 51
0.9%
3.333333333 1
 
< 0.1%
4 49
0.9%
5 35
 
0.6%
5.333333333 1
 
< 0.1%
5.666666667 1
 
< 0.1%
6 29
 
0.5%
6.142857143 1
 
< 0.1%
ValueCountFrequency (%)
74215 1
< 0.1%
40498.5 1
< 0.1%
14149 1
< 0.1%
13956 1
< 0.1%
7824 1
< 0.1%
6009.333333 1
< 0.1%
5963 1
< 0.1%
5197 1
< 0.1%
4300 1
< 0.1%
4282 1
< 0.1%

unique_basket_size
Real number (ℝ)

Distinct1242
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.377972
Minimum1
Maximum1109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:58.806235image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18.9212121
median17.7
Q334.6125
95-th percentile173
Maximum1109
Range1108
Interquartile range (IQR)25.691288

Descriptive statistics

Standard deviation76.661363
Coefficient of variation (CV)1.9468083
Kurtosis32.701861
Mean39.377972
Median Absolute Deviation (MAD)11.033333
Skewness5.0399239
Sum224730.08
Variance5876.9646
MonotonicityNot monotonic
2023-02-21T17:19:58.903825image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 287
 
5.0%
2 157
 
2.8%
3 116
 
2.0%
13 110
 
1.9%
10 103
 
1.8%
9 98
 
1.7%
11 96
 
1.7%
5 96
 
1.7%
6 93
 
1.6%
7 91
 
1.6%
Other values (1232) 4460
78.1%
ValueCountFrequency (%)
1 287
5.0%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
< 0.1%
1.5 9
 
0.2%
1.545454545 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
1.888888889 1
 
< 0.1%
ValueCountFrequency (%)
1109 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%
703 1
< 0.1%
686 1
< 0.1%
675 1
< 0.1%
673 1
< 0.1%
660 1
< 0.1%
649 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5463
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1801.1622
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:58.994695image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.33
Q1236.24
median612.78
Q31568.67
95-th percentile5312.424
Maximum279138.02
Range279137.6
Interquartile range (IQR)1332.43

Descriptive statistics

Standard deviation7889.3166
Coefficient of variation (CV)4.3801255
Kurtosis609.41668
Mean1801.1622
Median Absolute Deviation (MAD)478.61
Skewness21.650634
Sum10279233
Variance62241317
MonotonicityNot monotonic
2023-02-21T17:19:59.082005image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95 9
 
0.2%
2.95 8
 
0.1%
1.25 8
 
0.1%
4.95 8
 
0.1%
1.65 7
 
0.1%
3.75 7
 
0.1%
12.75 7
 
0.1%
7.5 6
 
0.1%
4.25 6
 
0.1%
5.95 6
 
0.1%
Other values (5453) 5635
98.7%
ValueCountFrequency (%)
0.42 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 4
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.25 8
0.1%
1.44 1
 
< 0.1%
1.65 7
0.1%
1.69 1
 
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
77183.6 1
< 0.1%
72882.09 1
< 0.1%

recency_days
Real number (ℝ)

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.91432
Minimum0
Maximum373
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:59.177414image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177

Descriptive statistics

Standard deviation111.5545
Coefficient of variation (CV)0.95415607
Kurtosis-0.64178678
Mean116.91432
Median Absolute Deviation (MAD)61
Skewness0.81381712
Sum667230
Variance12444.407
MonotonicityNot monotonic
2023-02-21T17:19:59.271077image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 110
 
1.9%
4 105
 
1.8%
3 98
 
1.7%
2 91
 
1.6%
10 86
 
1.5%
8 82
 
1.4%
17 79
 
1.4%
9 79
 
1.4%
7 78
 
1.4%
15 68
 
1.2%
Other values (294) 4831
84.7%
ValueCountFrequency (%)
0 38
 
0.7%
1 110
1.9%
2 91
1.6%
3 98
1.7%
4 105
1.8%
5 52
0.9%
7 78
1.4%
8 82
1.4%
9 79
1.4%
10 86
1.5%
ValueCountFrequency (%)
373 23
0.4%
372 22
0.4%
371 17
0.3%
369 4
 
0.1%
368 13
0.2%
367 16
0.3%
366 15
0.3%
365 19
0.3%
364 11
0.2%
362 7
 
0.1%

qtd_purchases
Real number (ℝ)

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4655686
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:59.373935image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.8029445
Coefficient of variation (CV)1.9630096
Kurtosis303.12306
Mean3.4655686
Median Absolute Deviation (MAD)0
Skewness13.212884
Sum19778
Variance46.280053
MonotonicityNot monotonic
2023-02-21T17:19:59.464160image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2878
50.4%
2 831
 
14.6%
3 502
 
8.8%
4 395
 
6.9%
5 236
 
4.1%
6 173
 
3.0%
7 139
 
2.4%
8 98
 
1.7%
9 68
 
1.2%
10 55
 
1.0%
Other values (46) 332
 
5.8%
ValueCountFrequency (%)
1 2878
50.4%
2 831
 
14.6%
3 502
 
8.8%
4 395
 
6.9%
5 236
 
4.1%
6 173
 
3.0%
7 139
 
2.4%
8 98
 
1.7%
9 68
 
1.2%
10 55
 
1.0%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
< 0.1%
60 1
< 0.1%

frequency
Real number (ℝ)

Distinct1224
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53768385
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:59.558180image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.011035505
Q10.024965374
median1
Q31
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.97503463

Descriptive statistics

Standard deviation0.53016563
Coefficient of variation (CV)0.98601739
Kurtosis160.88187
Mean0.53768385
Median Absolute Deviation (MAD)0
Skewness5.1973416
Sum3068.5617
Variance0.2810756
MonotonicityNot monotonic
2023-02-21T17:19:59.652498image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2939
51.5%
0.0625 18
 
0.3%
0.02777777778 17
 
0.3%
0.02380952381 16
 
0.3%
0.03448275862 15
 
0.3%
0.08333333333 15
 
0.3%
0.09090909091 15
 
0.3%
0.02941176471 14
 
0.2%
0.07692307692 13
 
0.2%
0.03571428571 13
 
0.2%
Other values (1214) 2632
46.1%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
< 0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
< 0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
< 0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%
1.142857143 1
 
< 0.1%
1 2939
51.5%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtd_products
Real number (ℝ)

Distinct439
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.6082
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-02-21T17:19:59.755304image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q113
median36
Q384
95-th percentile241
Maximum1786
Range1785
Interquartile range (IQR)71

Descriptive statistics

Standard deviation101.64772
Coefficient of variation (CV)1.4602837
Kurtosis43.945149
Mean69.6082
Median Absolute Deviation (MAD)28
Skewness4.7067571
Sum397254
Variance10332.259
MonotonicityNot monotonic
2023-02-21T17:19:59.855816image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 280
 
4.9%
2 149
 
2.6%
3 113
 
2.0%
10 101
 
1.8%
5 99
 
1.7%
9 96
 
1.7%
8 93
 
1.6%
11 93
 
1.6%
6 92
 
1.6%
7 90
 
1.6%
Other values (429) 4501
78.9%
ValueCountFrequency (%)
1 280
4.9%
2 149
2.6%
3 113
2.0%
4 90
 
1.6%
5 99
 
1.7%
6 92
 
1.6%
7 90
 
1.6%
8 93
 
1.6%
9 96
 
1.7%
10 101
 
1.8%
ValueCountFrequency (%)
1786 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
1109 1
< 0.1%
884 1
< 0.1%
816 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%

Interactions

2023-02-21T17:19:57.200354image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:50.894842image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.612275image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:52.866225image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.602980image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.315646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.025465image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.769836image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.461760image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.277525image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:50.968890image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.686062image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:52.943359image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.677699image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.390489image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.102767image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.842798image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.538642image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.356451image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.045189image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.756870image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.020528image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.754088image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.465554image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.178236image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.915971image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.616687image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.442347image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.127257image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.840734image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.105005image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.839412image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.547704image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.270398image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.996968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.702497image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.524613image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.202911image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.916532image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.185641image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.915346image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.625233image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.348630image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.072792image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.783494image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.607142image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.279365image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.992476image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.265501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.993614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.700590image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.432535image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.147067image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.862453image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.696546image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.363897image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:52.077481image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.352629image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.074671image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.784605image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.518471image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.228232image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.949566image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.773832image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.443015image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:52.149151image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.430232image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.149268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.858387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.596947image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.299121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.025414image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.862751image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:51.528923image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:52.785994image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:53.517591image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.231316image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:54.943205image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:55.683052image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:56.379980image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-21T17:19:57.113594image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-02-21T17:19:59.946160image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
customer_idticket_sizebasket_sizeunique_basket_sizegross_revenuerecency_daysqtd_purchasesfrequencyqtd_products
customer_id1.000-0.394-0.1460.112-0.1820.243-0.3840.386-0.018
ticket_size-0.3941.0000.255-0.3500.366-0.1820.323-0.276-0.160
basket_size-0.1460.2551.0000.6120.722-0.1980.157-0.1280.618
unique_basket_size0.112-0.3500.6121.0000.489-0.034-0.0490.0360.822
gross_revenue-0.1820.3660.7220.4891.000-0.4250.644-0.4570.794
recency_days0.243-0.182-0.198-0.034-0.4251.000-0.5950.490-0.325
qtd_purchases-0.3840.3230.157-0.0490.644-0.5951.000-0.8130.450
frequency0.386-0.276-0.1280.036-0.4570.490-0.8131.000-0.314
qtd_products-0.018-0.1600.6180.8220.794-0.3250.450-0.3141.000

Missing values

2023-02-21T17:19:57.985812image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-21T17:19:58.114794image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idticket_sizebasket_sizeunique_basket_sizegross_revenuerecency_daysqtd_purchasesfrequencyqtd_products
01234677183.6074215.001.0077183.6032511.001
11234723.10351.1426.004310.00270.02103
21234873.76583.005.751437.247540.0121
31234920.24630.0072.001457.551811.0072
41235018.40196.0016.00294.4031011.0016
51235218.7275.1411.001385.743670.0357
61235322.2520.004.0089.0020411.004
71235418.61530.0058.001079.4023211.0058
81235535.34240.0013.00459.4021411.0013
91235635.92524.3319.332487.432230.0152
customer_idticket_sizebasket_sizeunique_basket_sizegross_revenuerecency_daysqtd_purchasesfrequencyqtd_products
5697213639.011852.00675.006083.95111.00675
5698213649.562150.00748.007150.07111.00748
56992136518.16691.00203.003686.80111.00203
57002136678.061074.0055.004839.42111.0055
5701213672.5614.007.0017.90111.007
5702213681.682.002.003.35111.002
5703213698.991747.00634.005699.00111.00634
5704213709.252010.00730.006756.06011.00730
57052137154.53654.0056.003217.20011.0056
57062137218.21731.00217.003950.72011.00217